Epileptic Absence Seizures EEG Wavelet Analysis And Regional Feature Extraction

Objective: Epilepsy is a common disease of the nervous system, its diagnosis and treatmenthas been the problem of the medical profession, often used in clinical electroencephalogram(EEG) as a means of epilepsy diagnosis and evaluation of treatment efficacy. EEG by themethod of artificial knowledge map, less efficient, but also by the knowledge map their ownlevel of this experiment, expect to find a computer-aided analysis of EEG method; seizuresEEG sequence the variation of the signal.Methods: Based on Matlab software, the use of wavelet analysis method in patients withepilepsy EEG signals quantitative analysis, software programming, apply the wavelet energy,the wavelet variance, wavelet entropy function can be obtained wavelet decomposition ofeach goal lead after beta, alpha theta of four waveforms delta wavelet energy wavelet variance,wavelet entropy values, individual values can be analyzed using statistical software; softwarefeatures modeling, try to identify the waveform absence seizures; modeling on the attack early,The ictal EEG energy analysis of the changes.Results:1. Bilateral hemisphere wavelet entropy, wavelet variance analysis of the waveletenergy indicators, absence seizures on both sides of the brain electrophysiological activitiesconsistent.2ictal and interictal attack compared to the pre-beta, alpha, theta, delta four kindsof basic waveform wavelet variance were significantly increased. Comparison of waveletentropy in the attack process, The absence seizures EEG emerged as a result of synchronizedelectrical physiological processes.4. Significant difference in exacerbation of wavelet energybackground energy, the absence recognition model, proved by the energy difference of theseizures effectively identify. Absence seizures, the basic waveform wavelet energy is notconstant trend.Conclusion: The experiments revealed that the the absence seizures process mathematicalcharacteristics change, in order to explain the deep-seated inherent characteristics and causesof the symptoms of epilepsy provide an objective basis. While taking advantage of thesecharacteristic changes automatically identify waveform absence seizures, epilepsy absenceseizures EEG successfully identify, to provide a new method for the automated analysis ofclinical EEG has good practical value and social benefits.